Aswaat vs Data Scientific

The Quran was not revealed in a single recitation. It was revealed in ten, each one transmitted with unbroken chains of authority stretching back to the Prophet (peace be upon him). For over a millennium, scholars have preserved these differences with extraordinary precision. Most people have never seen them. Aswaat changes that. Compare riwaayaat, directly on the mushaf. Hafs is always your base layer. Overlay Warsh, Qaaloon, or any other riwaayah, and every word that differs is highlighted in color, right on the page, in context, exactly where it appears. Select two riwaayaat at once, each with its own color, and see their differences side by side. No cross-referencing. No footnotes. Just the text, made visible. Read in the script that is yours. Aswaat supports both IndoPak script, the typeface printed in masaahif across South Asia, East Africa, and beyond, and the traditional Uthmani script. Whichever you learned with, Aswaat reads the way you do. Hear the tradition, not just the name. Our reciter library is organized by riwayaah, not by country or popularity. Find reciters of Warsh, Qaaloon, Ad-Duri, and more, and hear precisely how each riwayaah is recited by those who have mastered it. Learn the science properly. Aswaat includes a growing library of educational videos covering two of the most important foundations of Quranic study: Tajweed - the rules of correct recitation, explained clearly for students at every level, from the foundations of makhaarij to the subtleties of idghaam and mad. History of the Qiraat - who were the ten Imams? How were the riwaayaat transmitted? What is the difference between a qiraah, a riwayaah, and a tareeq? These questions matter, and Aswaat takes them seriously. For students, scholars, and everyone who loves the Quran and its sciences, Aswaat is for you.

Science of Data is a mobile companion to two graduate-level books by Jean Claude Lemoyne Zabaloff — "The Science of Data" and "Data Science Project" — paired with an AI-powered ML Code Generator that produces complete, runnable scripts in Python, R, or MATLAB for any machine learning task you describe. 13 CHAPTERS What is AI? · Foundations · Supervised Learning · Unsupervised Learning · Reinforcement Learning · Deep Learning · Bayesian Inference · Support Vector Machines · Data Science · Knowledge · DS Process · Key Concepts 5 APPENDICES A — Python | B — R | C — MATLAB / Octave / Scilab D — Optimization | E — Public Data Sources ML CODE GENERATOR Describe any ML task or pick from 23 built-in templates and receive a complete, runnable script instantly. Languages — Python, R, MATLAB Templates — Classifiers · Regression · Cross-Validation · ROC Curve · Confusion Matrix · Feature Importance · Pipeline · Clustering · Neural Network · SVM · PCA · Preprocessing · Hyperparameter Tuning · Time Series · Bayesian Inference · Naive Bayes · Bayesian Regression · Metropolis-Hastings · Gibbs Sampling · MCMC Diagnostics · Gradient Descent · Constrained Optimization · Bayesian Optimization Every script includes type hints, WHY comments, labelled output, an expected-output block, and an install-command badge (pip install / install.packages / Requires:). Copy to clipboard or share via any app. Three AI-generated follow-up questions deepen understanding after every session. APP FEATURES · Full-text search across all chapters and appendices · Bookmarks for quick chapter access · Font size control: S / M / L / XL · Dark and light mode · Swipe navigation between chapters · Content tab + Notes / cheat-sheet tab per chapter · GFM table rendering with horizontal scroll · Dracula syntax highlighting — light-green comments, orange function calls, pink definitions, purple numbers IDEAL FOR Graduate ML and data science students, practitioners who want a rigorous pocket reference, engineers moving into ML, and anyone studying Bayesian or probabilistic methods. PRIVACY No account required. No personal data collected or stored. The ML Code Generator sends only your prompt text to Anthropic's Claude API; no personal information is ever transmitted.